{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# lab2 信息比率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.调用聚宽JointQuant接口获取5支场外基金数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "print(datetime.date.today())\n",
    "print(isauth)\n",
    "print(get_query_count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "codes = ['000003', '000005', '000014', '000015', '000024']\n",
    "start_date = datetime.date(2018, 1, 1)\n",
    "end_date = datetime.date(2018, 12, 31)\n",
    "\n",
    "\n",
    "def get_data():\n",
    "    #     codes = ['000003', '000005', '000007', '000014', '000015']\n",
    "    #     start_date = datetime.date(2018, 1, 1)\n",
    "    #     end_date = datetime.date(2018, 12, 31)\n",
    "\n",
    "    for code in codes:\n",
    "        q = (\n",
    "            query(finance.FUND_NET_VALUE)\n",
    "            .filter(finance.FUND_NET_VALUE.code == code)\n",
    "            .order_by(finance.FUND_NET_VALUE.day.desc())\n",
    "        )\n",
    "        df = finance.run_query(q)\n",
    "        df.to_csv(f'{code}.csv', index=False)\n",
    "    print('success!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df1 = pd.read_csv('000003.csv')\n",
    "df2 = pd.read_csv('000005.csv')\n",
    "df3 = pd.read_csv('000014.csv')\n",
    "df4 = pd.read_csv('000015.csv')\n",
    "df5 = pd.read_csv('000024.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "245 245 245 245 245\n"
     ]
    }
   ],
   "source": [
    "start_date = '2018-01-01'\n",
    "end_date = '2018-12-31'\n",
    "\n",
    "df1 = df1[(start_date <= df1['day']) & (\n",
    "    df1['day'] <= end_date)].sort_values(by=['day'])\n",
    "df2 = df2[(start_date <= df2['day']) & (\n",
    "    df2['day'] <= end_date)].sort_values(by=['day'])\n",
    "df3 = df3[(start_date <= df3['day']) & (\n",
    "    df3['day'] <= end_date)].sort_values(by=['day'])\n",
    "df4 = df4[(start_date <= df4['day']) & (\n",
    "    df4['day'] <= end_date)].sort_values(by=['day'])\n",
    "df5 = df5[(start_date <= df5['day']) & (\n",
    "    df5['day'] <= end_date)].sort_values(by=['day'])\n",
    "print(len(df1), len(df2), len(df3), len(df4), len(df5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>money</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6222.93</td>\n",
       "      <td>6265.66</td>\n",
       "      <td>6266.53</td>\n",
       "      <td>6222.93</td>\n",
       "      <td>2.826537e+09</td>\n",
       "      <td>2.823900e+10</td>\n",
       "      <td>0.006867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6268.45</td>\n",
       "      <td>6280.94</td>\n",
       "      <td>6300.14</td>\n",
       "      <td>6268.07</td>\n",
       "      <td>2.960170e+09</td>\n",
       "      <td>2.803178e+10</td>\n",
       "      <td>0.001993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6282.31</td>\n",
       "      <td>6293.58</td>\n",
       "      <td>6296.37</td>\n",
       "      <td>6277.98</td>\n",
       "      <td>4.019659e+09</td>\n",
       "      <td>3.103672e+10</td>\n",
       "      <td>0.001794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6296.20</td>\n",
       "      <td>6304.45</td>\n",
       "      <td>6309.44</td>\n",
       "      <td>6289.88</td>\n",
       "      <td>3.040324e+09</td>\n",
       "      <td>2.723033e+10</td>\n",
       "      <td>0.001310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6302.06</td>\n",
       "      <td>6316.85</td>\n",
       "      <td>6321.57</td>\n",
       "      <td>6296.57</td>\n",
       "      <td>2.559710e+09</td>\n",
       "      <td>2.477497e+10</td>\n",
       "      <td>0.002347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>5514.09</td>\n",
       "      <td>5528.43</td>\n",
       "      <td>5529.96</td>\n",
       "      <td>5505.45</td>\n",
       "      <td>4.018301e+09</td>\n",
       "      <td>3.559954e+10</td>\n",
       "      <td>0.002601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>5509.21</td>\n",
       "      <td>5507.23</td>\n",
       "      <td>5518.42</td>\n",
       "      <td>5465.12</td>\n",
       "      <td>5.710953e+09</td>\n",
       "      <td>3.732280e+10</td>\n",
       "      <td>-0.000359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>5505.07</td>\n",
       "      <td>5496.30</td>\n",
       "      <td>5514.48</td>\n",
       "      <td>5489.44</td>\n",
       "      <td>4.275749e+09</td>\n",
       "      <td>3.057239e+10</td>\n",
       "      <td>-0.001593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>5523.93</td>\n",
       "      <td>5489.63</td>\n",
       "      <td>5529.09</td>\n",
       "      <td>5486.45</td>\n",
       "      <td>4.176552e+09</td>\n",
       "      <td>2.890790e+10</td>\n",
       "      <td>-0.006209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>5494.57</td>\n",
       "      <td>5499.31</td>\n",
       "      <td>5514.79</td>\n",
       "      <td>5488.15</td>\n",
       "      <td>4.891288e+09</td>\n",
       "      <td>4.207437e+10</td>\n",
       "      <td>0.000863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        open    close     high      low        volume         money      rate\n",
       "0    6222.93  6265.66  6266.53  6222.93  2.826537e+09  2.823900e+10  0.006867\n",
       "1    6268.45  6280.94  6300.14  6268.07  2.960170e+09  2.803178e+10  0.001993\n",
       "2    6282.31  6293.58  6296.37  6277.98  4.019659e+09  3.103672e+10  0.001794\n",
       "3    6296.20  6304.45  6309.44  6289.88  3.040324e+09  2.723033e+10  0.001310\n",
       "4    6302.06  6316.85  6321.57  6296.57  2.559710e+09  2.477497e+10  0.002347\n",
       "..       ...      ...      ...      ...           ...           ...       ...\n",
       "238  5514.09  5528.43  5529.96  5505.45  4.018301e+09  3.559954e+10  0.002601\n",
       "239  5509.21  5507.23  5518.42  5465.12  5.710953e+09  3.732280e+10 -0.000359\n",
       "240  5505.07  5496.30  5514.48  5489.44  4.275749e+09  3.057239e+10 -0.001593\n",
       "241  5523.93  5489.63  5529.09  5486.45  4.176552e+09  2.890790e+10 -0.006209\n",
       "242  5494.57  5499.31  5514.79  5488.15  4.891288e+09  4.207437e+10  0.000863\n",
       "\n",
       "[243 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_index = pd.read_csv('index.csv')\n",
    "df_index['rate'] = (df_index['close'] - df_index['open']) / df_index['open']\n",
    "df_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.0530267 ,  0.06850708,  0.00967456,  0.05571733,  0.09889241])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "base_rate = df_index['rate']\n",
    "rate1 = np.asarray([df1.iloc[i, 3] - df1.iloc[i-1, 3]\n",
    "                    for i in range(1, len(df1))])[:-1]\n",
    "rate2 = np.asarray([df2.iloc[i, 3] - df2.iloc[i-1, 3]\n",
    "                    for i in range(1, len(df2))])[:-1]\n",
    "rate3 = np.asarray([df3.iloc[i, 3] - df3.iloc[i-1, 3]\n",
    "                    for i in range(1, len(df3))])[:-1]\n",
    "rate4 = np.asarray([df4.iloc[i, 3] - df4.iloc[i-1, 3]\n",
    "                    for i in range(1, len(df4))])[:-1]\n",
    "rate5 = np.asarray([df5.iloc[i, 3] - df5.iloc[i-1, 3]\n",
    "                    for i in range(1, len(df5))])[:-1]\n",
    "# print(len(rate1), len(rate2), len(rate3), len(rate4), len(rate5), len(base_rate))\n",
    "\n",
    "# TD_t1 = rate1 - base_rate\n",
    "# TD_t2 = rate2 - base_rate\n",
    "# TD_t3 = rate3 - base_rate\n",
    "# TD_t4 = rate4 - base_rate\n",
    "# TD_t5 = rate5 - base_rate\n",
    "\n",
    "# TD_1 = np.average(TD_t1)\n",
    "# TD_2 = np.average(TD_t2)\n",
    "# TD_3 = np.average(TD_t3)\n",
    "# TD_4 = np.average(TD_t4)\n",
    "# TD_5 = np.average(TD_t5)\n",
    "\n",
    "n = 243\n",
    "TD_ti = np.asarray([rate1, rate2, rate3, rate4, rate5]) - np.asarray(base_rate)\n",
    "TD_i = np.average(TD_ti, axis=1).reshape(5, 1)\n",
    "TE_i = np.sqrt(1 / (n-1) * np.sum(np.square(TD_ti - TD_i), axis=1)).reshape(5, 1)\n",
    "IR_i = TD_i / TE_i\n",
    "IR_i = IR_i.reshape(5)\n",
    "IR_i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "            \"hoverAnimation\": true,\n",
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       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
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       "            }\n",
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       "                239,\n",
       "                240,\n",
       "                241,\n",
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       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"TD_ti\"\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_d025e25c0da2410cb3af69fd11919191.setOption(option_d025e25c0da2410cb3af69fd11919191);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1b0156da550>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyecharts.options as opts\n",
    "from pyecharts.charts import Line\n",
    "from pyecharts.faker import Faker\n",
    "\n",
    "c = (\n",
    "    Line()\n",
    "    .add_xaxis(list(range(n)))\n",
    "    .add_yaxis(\"中海可转债债券\", list(TD_ti[0, :]))\n",
    "    .add_yaxis(\"嘉实增强信用\", list(TD_ti[1, :]))\n",
    "    .add_yaxis(\"华夏聚利债券\", list(TD_ti[2, :]))\n",
    "    .add_yaxis(\"华夏纯债债券\", list(TD_ti[3, :]))\n",
    "    .add_yaxis(\"大摩双利增强债券\", list(TD_ti[4, :]))\n",
    "#     .add_yaxis(\"商家B\", Faker.values())\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"TD_ti\"))\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "    .render_notebook()\n",
    ")\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"ebd5204cd22046e887d5a4de22953ee0\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_ebd5204cd22046e887d5a4de22953ee0 = echarts.init(\n",
       "                    document.getElementById('ebd5204cd22046e887d5a4de22953ee0'), 'white', {renderer: 'canvas'});\n",
       "                var option_ebd5204cd22046e887d5a4de22953ee0 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"name\": \"2018\\u5e74\",\n",
       "            \"data\": [\n",
       "                -0.053026695088300206,\n",
       "                0.06850707980557787,\n",
       "                0.009674559377905276,\n",
       "                0.055717334363951015,\n",
       "                0.09889240621696892\n",
       "            ],\n",
       "            \"barCategoryGap\": \"20%\",\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"2018\\u5e74\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"2018\\u5e74\": true\n",
       "            },\n",
       "            \"show\": true\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"axisLabel\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"rotate\": -15,\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"\\u4e2d\\u6d77\\u53ef\\u8f6c\\u503a\\u503a\\u5238\",\n",
       "                \"\\u5609\\u5b9e\\u589e\\u5f3a\\u4fe1\\u7528\",\n",
       "                \"\\u534e\\u590f\\u805a\\u5229\\u503a\\u5238\",\n",
       "                \"\\u534e\\u590f\\u7eaf\\u503a\\u503a\\u5238\",\n",
       "                \"\\u5927\\u6469\\u53cc\\u5229\\u589e\\u5f3a\\u503a\\u5238\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u4fe1\\u606f\\u6bd4\\u7387\",\n",
       "            \"subtext\": \"5\\u652f\\u57fa\\u91d1\\u6bd4\\u8f83\"\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_ebd5204cd22046e887d5a4de22953ee0.setOption(option_ebd5204cd22046e887d5a4de22953ee0);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1b0158080f0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Bar\n",
    "\n",
    "c = (\n",
    "    Bar()\n",
    "    .add_xaxis(\n",
    "        [\n",
    "            \"中海可转债债券\",\n",
    "            \"嘉实增强信用\",\n",
    "            \"华夏聚利债券\",\n",
    "            \"华夏纯债债券\",\n",
    "            \"大摩双利增强债券\",\n",
    "        ]\n",
    "    )\n",
    "    .add_yaxis(\"2018年\", list(IR_i))\n",
    "    .set_global_opts(\n",
    "        xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),\n",
    "        title_opts=opts.TitleOpts(title=\"信息比率\", subtitle=\"5支基金比较\"),\n",
    "    )\n",
    "    .render_notebook()\n",
    ")\n",
    "c"
   ]
  }
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